Heat indices for file systems and block storage

ABSTRACT

Techniques and mechanisms are provided to allow for selective optimization, including deduplication and/or compression, of portions of files and data blocks. Data access is monitored to generate a heat index for identifying sections of files and volumes that are frequently and infrequently accessed. These frequently used portions may be left non-optimized to reduce or eliminate optimization I/O overhead. Infrequently accessed portions can be more aggressively optimized.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit under 35 U.S.C. §120 to U.S. applicationSer. No. 13/041,268 filed Mar. 4, 2011 and titled “HEAT INDICES FOR FILESYSTEMS AND BLOCK STORAGE”, which has been issued as U.S. Pat. No.8,612,401, which claims the benefit under 35 U.S.C. 119(e) of U.S.Provisional Application No. 61/329,016 filed Apr. 28, 2010 and titled“ACTIVE OPTIMIZATION AND HEAT INDICES FOR FILE SYSTEMS AND BLOCKSTORAGE,” the entirety of both of which are incorporated herein byreference for all purposes.

TECHNICAL FIELD

The present disclosure relates to heat indices for file systems andblock storage.

DESCRIPTION OF RELATED ART

Data optimization reduces system administration and infrastructurecosts. Data optimization may involve compression and deduplication.Compression involves re-encoding data in a form that uses fewer bitsthan the original data. Data deduplication refers to the ability of asystem to eliminate data duplication across files to increase storage,transmission, and/or processing efficiency. A storage system whichincorporates deduplication technology involves storing a single instanceof a data segment that is common across multiple files. In someexamples, data sent to a storage system is segmented in fixed orvariable sized segments. Each segment is provided with a segmentidentifier (ID), such as a digital signature or a hash of the actualdata. Once the segment ID is generated, it can be used to determine ifthe data segment already exists in the system. If the data segment doesexist, it need not be stored again.

However, data optimization can entail overhead both during dataoptimization and during processing of optimized data. For example,optimization causes an increase in I/O access overhead. Consequently,techniques and mechanisms are provided for reducing optimizationoverhead.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular embodiments of the present invention.

FIG. 1 illustrates a particular example of network that can use thetechniques and mechanisms of the present invention.

FIG. 2 illustrates one example of a technique for performingoptimization.

FIG. 3 illustrates one example of a technique for handling file accessin a file system.

FIG. 4A illustrates a particular example of a filemap.

FIG. 4B illustrates a particular example of a datastore suitcase.

FIG. 5 illustrates a particular example of a deduplication dictionary.

FIG. 6A illustrates a particular example of a file having a single datasegment.

FIG. 6B illustrates a particular example of a file having multiple datasegments and components.

FIG. 7 illustrates a particular example of a computer system.

DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.

For example, the techniques and mechanisms of the present invention willbe described in the context of particular files. However, it should benoted that the techniques and mechanisms of the present invention applyto a variety of different data constructs including files, blocks, etc.In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention.Particular example embodiments of the present invention may beimplemented without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present invention.

Various techniques and mechanisms of the present invention willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present inventionunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present invention will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

Techniques and mechanisms are provided to allow for selectiveoptimization, including deduplication and/or compression, of portions offiles and data blocks. Data access is monitored to generate a heat indexfor identifying sections of files and volumes that are frequently andinfrequently accessed. These frequently used portions may be leftnon-optimized to reduce or eliminate optimization I/O overhead.Infrequently accessed portions can be more aggressively optimized.

Example Embodiments

Maintaining, managing, transmitting, and/or processing large amounts ofdata can have significant costs. These costs include not only power andcooling costs but system maintenance, network bandwidth, and hardwarecosts as well.

Some efforts have been made to reduce the footprint of data maintainedby file servers and reduce the associated network traffic. A variety ofutilities compress files on an individual basis prior to writing data tofile servers. Compression algorithms are well developed and widelyavailable. Some compression algorithms target specific types of data orspecific types of files. Compressions algorithms operate in a variety ofmanners, but many compression algorithms analyze data to determinesource sequences in data that can be mapped to shorter code words. Inmany implementations, the most frequent source sequences or the mostfrequent long source sequences are replaced with the shortest possiblecode words.

Data deduplication reduces storage footprints by reducing the amount ofredundant data. Deduplication may involve identifying variable or fixedsized segments. According to various embodiments, each segment of datais processed using a hash algorithm such as MD5 or SHA-1. This processgenerates a unique ID, hash, or reference for each segment. That is, ifonly a few bytes of a document or presentation are changed, only changedportions are saved. In some instances, a deduplication system searchesfor matching sequences using a fixed or sliding window and usesreferences to identify matching sequences instead of storing thematching sequences again.

In a data deduplication system, the backup server working in conjunctionwith a backup agent identifies candidate files for backup, creates abackup stream and sends the data to the deduplication system. A typicaltarget system in a deduplication system will deduplicate data as datasegments are received. A block that has a duplicate already stored onthe deduplication system will not need to be stored again. However,other information such as references and reference counts may need to beupdated. Some implementations allow the candidate data to be directlymoved to the deduplication system without using backup software byexposing a NAS drive that a user can manipulate to backup and archivefiles.

The techniques and mechanisms of the present invention recognize thatoptimization I/O overhead can be significant. Decompressing and/orreflating data that has been optimized to make a read or write accesscan take significant processing resources. The techniques and mechanismsof the present invention recognize that particular portions of files aretypically more frequently accessed than other portions. According tovarious embodiments, a system monitors access frequency of portions of afile. Although the term file is used throughout, it will be recognizedthat the techniques of the present invention apply to variety of formsof data such as data blocks. The term file will be used herein toinclude not just files, but variations of files and other organizedgroupings of data such as data blocks. Portions of a file that areheavily written to or constantly updated, also referred to herein as hotsections of a file, are not subject to the optimization process or aresubject to a lighter weight optimization process. This reduces I/Oaccess overhead of hot sections. Portions that are less frequentlyaccessed can be optimized while portions that are least frequentlyaccessed can be very aggressively optimized.

Conventional optimization involves deduplication and compression and isimplemented on entire files without evaluating the frequency of use ofparticular file portions.

In particular embodiments, a system uses a variable heat index so thatas different sections of the files and volume get hotter over differentperiods of time, the system adapts naturally to reduce the I/O accesspenalty for those frequently accessed sections.

According to various embodiments, it is recognized that newly writtendata is likely to be accessed in the near future. Furthermore, it isrecognized that post process benefits from having no write penalty.Thus, the following algorithm will outline how a live file is ingestedand optimized, even while file or block I/O is occurring.

While the description below is specific to files, the concept will applyto specific blocks of a volume in a block storage device as well, eventhough the description focuses on files.

Algorithm_Optimize_File(FILE)

BEGIN

Get_Optimizable_Ranges(FILE, 0, EOF, RANGES)

FOR (i in RANGES) DO

BEGIN

According to various embodiments, it is first determined whether thisrange for this file has been optimized before. If this range haspreviously been optimized, this implies that the file system hadexternally modified this range and dead (unreferenced) data is beingrepresented in the optimization engine. The unreferenced data needs tobe cleaned up.

Cleanup_Optimized_Ranges_If_Any(FILE, RANGES[i].START, RANGES[i].END)

In particular embodiments, it is then determined if the region was veryrecently modified. If it was, then we can avoid optimizing this rangesince it is likely to be read and modified again imminently.

IF Test_Last_Modified_Stamp(RANGES[i].TIME_STAMP) THEN CONTINUE

This function will potentially find a smaller range size based on theactual implementation

END=Find_Optimimal_Range_End(FILE, RANGES[i].START, RANGES[i].END)

Acquire_Range_lock(FILE, RANGES[i].START, END, TOKEN)

The function is implementation specific and will actually perform theoptimization:

Do_Optimization(FILE, RANGES[i].START, END, RANGE_ID)

According to various embodiments, the file width range is committed sothat the file system knows the range has been successful optimized. Inparticular embodiments, the file system records the RANGE_ID for thisrange. It may be needed by the optimization engine to satisfy a readrequest to that range.

Commit_Range_Lock(FILE, RANGES[i].START, END, TOKEN, RANGE_ID,SUCCESS_CODE)

According to various embodiments, the success code is tested. If thefile was modified while optimization was occurring, then theoptimization performed has to be aborted. File access duringoptimization can be detected by implementation specific data maintainedin the TOKEN parameter and is decided by the file system. In particularembodiments, cleanup can be performed here or left to step one of thisprocess.

Test_And_Cleanup(FILE, RANGES[i].START, END, TOKEN RANGE_ID,SUCCESS_CODE)

END

END

The above algorithm may be run on every file in the file system,continually, even visiting files it has already visited. The process canbe run with varying degrees of frequency and aggressiveness.

According to various embodiments, the File System itself is responsiblefor maintaining ranges of a file and indications of whether any offsetswithin those ranges have been modified. The granularity of a range isleft to the discretion of the implementation. In one implementation,variable range sizes may be used. In another implementation, fixed widthrange sizes may be used. In the case of fixed width range sizes, even ifany byte of that range has been modified, the entire range is marked asdirty and will be made available in the call to Get_Optimizable_Ranges().

Whenever a write operation happens to a range, the file system directsthe write to the original file in the specified ranges, and flips theoptimization bits of those ranges to 0. This indicates that those rangesare now non-optimized (or at least contain partially newly writtendata).

These ranges will be picked up during a subsequent visit of that file bythe optimization engine through the Get_Optimizable_Ranges( ) method. Ifthe timestamp of that range is old enough, it will be optimized.

On any read operation, the file system will consult the optimization bitfor that range. If the bit is set, then the file system will referencethe RANGE_ID if needed and provide that to the optimization engine. Theoptimization engine can use the RANGE_ID as a pointer to the optimizeddata and perform the un-optimization processing.

In particular embodiments, the above approach can successfully handlewriting to an optimized file without going to the optimization engineand hence incurring an optimization overhead. The writes are directlyhandled by the file system and the file is modified without theknowledge of the optimization engine. When a read or write occurs, thefile system has enough information stored in the range specific datastructures to know where the data needs to be read from. The writes cango straight to the original file and bypass the optimization engine.Hence there is no equivalent of a Write_Range( ) function in thisapparatus.

According to various embodiments, any shift in the heat of the file issuccessfully handled by this approach. As different ranges in a file getcolder over time, the timestamp captures the age of that range. If arange is old enough, it will be optimized as can be seen in the innerloop. As previously cold ranges are written to, there is no writepenalty. And since the timestamp will capture the new age of that range,it will not be optimized, thus all reads to that freshly written rangewill go to the non-optimized file and handled directly by the filesystem with no additional penalty.

Finally, this scheme easily demonstrates how an existing live file, ofwhich no section has been optimized, can be ingested in whole, or inpart, into the optimization engine.

In many real world cases, such as virtual machine applications ordatabases, some portions of a file are in constant use. Analyzing thealgorithm above demonstrates how those ranges are not optimized, nomatter how small the threshold for the range timestamp.

FIG. 1A illustrates a particular example of a network that can use thetechniques and mechanisms of the present invention. Hosts 101, 103, 105,and 107 are connected to file servers 121, 123, and 125 through anetwork 111. Hosts may include computer systems, application servers,devices, etc. A network 111 may be a single network or a combination ofdifferent networks. According to various embodiments, each host 101,103, 105, and 107 runs applications that require data storage. The fileservers 121, 123, and 125 provide data storage through active storagemechanisms such as disk arrays. One example of active storage is aRedundant Array of Individual Disks (RAID) 151 connected to file server123 through storage area network (SAN) 141. The file servers 121, 123,and 125 also provide data storage through passive storage mechanismssuch as tape devices 161 and 163, and virtual tape device 165.

According to various embodiments, hosts 101, 103, 105, and 107 areconnected to file servers 121, 123, and 125 using file level protocolssuch as Server Message Block (SMB), Network File System (NFS), or theAndrew File System (AFS) that are capable of providing network attachedstorage (NAS) to heterogeneous clients. In particular examples, NASincludes both a file system and storage. SMB, NFS, and AFS generallyallow hosts 101, 103, 105, and 107 to access data at the file level. Thefile servers 121, 123, and 125 then use block level protocols such asserial advanced technology attachment (SATA), Internet Small ComputerSystems Interface (iSCSI), and storage area networks (SANs) to accessindividual blocks of data.

Block level protocols generally do not provide any file systemcapabilities to file servers but instead leave file system operations onthe application server side. The data accessed by the file servers 121,123, and 125 may be physically stored on direct attached storage 131,133, and 135, such as hard drives included in the corresponding fileservers. Alternatively, the data may be physically stored on tapedevices 161 or 163, or on virtual tape device 165. A virtual tape device165 may be implemented as an array of disks. The data may also be storedon RAID 151 connected over a SAN 141.

According to various embodiments, a segment ID index may be implementedat hosts 101, 103, 105, and 107, at network 111, or at file servers 121,123, and 125 or at a combination of entities. The segment ID generatorintercepts requests to store a data segment and determines whether thedata segment has already been stored at a target system. For example, ifa client associated with host 101 requests deduplication of multiplefiles in a directory, the segment ID generator determines what segmentsin the multiple files have already been deduplicated. For segmentsalready deduplicated, references and reference counts may be updated,but the data segments need not be transmitted again to the target systemfor deduplication. The determination can be made by comparing segmentIDs or hashes of segments for deduplication.

FIG. 2 illustrates one example of a technique for monitoring accessfrequency. According to various embodiments, a file portion size isdetermined at 201. In particular embodiments, a file portion size may beselected by a user or set empirically. In some examples, a file portionsize is based on the type of file. A database file may include groups ofrecords that may differ in access frequency. The file portion size maybe determined based on the size of a group of records. In otherexamples, a geospatial data file may include different map layers andeach file portion may correspond to individual map layers. In otherexamples, a file portion size may be set to a default of 2% of totalfile size. According to various embodiments, access frequency to thevarious portions of the file is monitored at 203. In particularembodiments, access includes processor write access and read access.According to various embodiments, a heat index is generated at 205 basedon the frequency of access.

In some examples, a high heat index may correspond to a very frequentlyaccessed portion. A low heat index may correspond to an infrequentlyaccessed portion. A high heat index may correspond to a timestampindicating a very recent access, or a count indicating a substantialnumber of accesses within a predetermined period of time. Similarly, alower heat index may correspond to a stale timestamp indicating norecent access, or a count indicating an insubstantial number of accesseswithin the predetermined period of time.

At 207, a request to optimize the file is received. Optimization mayinclude deduplication and/or compression. It is recognized that onlyinfrequently accessed portions should be optimized in order to reduceoptimization I/O overhead. Infrequently accessed portions can be moreaggressively optimized. According to various embodiments, anoptimization scale is applied at 209 based on the heat index duringoptimization of each file portion. A portion that is very infrequentlyaccessed can be very aggressively optimized. Aggressive optimization mayentail optimization with a significant data reduction ratio to achievethe smallest possible file size. Less aggressive optimization may entailoptimization with smaller data reduction ratios that achieve less filesize reduction. Very frequently accessed portions are left non-optimizedto reduce optimization I/O overhead at 211.

FIG. 3 illustrates one technique for handling file access. According tovarious embodiments, variable/fixed file ranges are established at 301.Whenever a write operation happens to a range at 303, the file systemdirects the write to the original file in the specified ranges at 305,maintains timestamp information at 307, and flips the optimization bitsof those ranges to 0 at 309. This indicates that those ranges are nownon-optimized (or at least contain partially newly written data).

These ranges will be picked up during a subsequent visit of that file bythe optimization engine through the Get_Optimizable_Ranges( ) method. Ifthe timestamp of that range is old enough, it will be optimized.

On any read operation at 311, the file system will consult theoptimization bit for that range at 313. If the bit is set, then the filesystem will reference the RANGE_ID if needed and provide that to theoptimization engine at 315. The optimization engine can use the RANGE_IDas a pointer to the optimized data and perform the de-optimizationprocessing 317. The de-optimized data is provided in response to theread request at 319

The above approach can successfully handle writing to an optimized filewithout going to the optimization engine and hence incurring anoptimization overhead. The writes are directly handled by the filesystem and the file is modified without the knowledge of theoptimization engine. When a read or write occurs, the file system hasenough information stored in the range specific data structures to knowwhere the data needs to be read from. In all cases, the writes gostraight to the original file and bypass the optimization engine. Hencethere is no equivalent of a Write_Range( ) method in this apparatus.

Furthermore, any shift in the heat of the file is successfully handledby this approach. As different ranges in a file get colder over time,the timestamp captures the age of that range. If a range is old enough,it will be optimized as can be seen in the inner loop. As previouslycold ranges are written to, there is no write penalty. And since thetimestamp will capture the new age of that range, it will not beoptimized, thus all reads to that freshly written range will go to thenon-optimized file and handled directly by the file system with noadditional penalty.

Finally, this scheme easily demonstrates how an existing live file, ofwhich no section has been optimized, can be ingested in whole, or inpart, into the optimization engine.

In many real world cases, such as virtual machine applications ordatabases, some portions of a file are in constant use. Analyzing thealgorithm above easily demonstrates how those ranges are not optimized,no matter how small the threshold for the range timestamp.

FIG. 4A illustrates one example of a filemap and FIG. 4B illustrates acorresponding datastore suitcase created after optimizing a file X.Filemap file X 401 includes offset 403, index 405, and lname 407 fields.According to various embodiments, each segment in the filemap for file Xis 8K in size. In particular embodiments, each data segment has an indexof format <Datastore Suitcase ID>. <Data Table Index>. For example, 0.1corresponds to suitcase ID 0 and datatable index 1, while 2.3corresponds to suitcase ID 2 and database index 3. The segmentscorresponding to offsets OK, 8K, and 16K all reside in suitcase ID 0while the data table indices are 1, 2, and 3. The lname field 407 isNULL in the filemap because each segment has not previously beenreferenced by any file.

FIG. 4B illustrates one example of a datastore suitcase corresponding tothe filemap file X 401. According to various embodiments, datastoresuitcase 471 includes an index portion and a data portion. The indexsection includes indices 453, data offsets 455, and data referencecounts 457. The data section includes indices 453, data 461, and lastfile references 463. According to various embodiments, arranging a datatable 451 in this manner allows a system to perform a bulk read of theindex portion to obtain offset data to allow parallel reads of largeamounts of data in the data section.

According to various embodiments, datastore suitcase 471 includes threeoffset, reference count pairs which map to the data segments of thefilemap file X 401. In the index portion, index 1 corresponding to datain offset-data A has been referenced once. Index 2 corresponding to datain offset-data B has been referenced once. Index 3 corresponding to datain offset-data C has been referenced once. In the data portion, index 1includes data A and a reference to File X 401 which was last to place areference on the data A. Index 2 includes data B and a reference to FileX 401 which was last to place a reference on the data B. Index 3includes data C and a reference to File X 401 which was last to place areference on the data C.

According to various embodiments, the dictionary is a key for thededuplication system. The dictionary is used to identify duplicate datasegments and point to the location of the data segment. When numeroussmall data segments exist in a system, the size of a dictionary canbecome inefficiently large. Furthermore, when multiple optimizers nodesare working on the same data set they will each create their owndictionary. This approach can lead to suboptimal deduplication since afirst node may have already identified a redundant data segment but asecond node is not yet aware of it because the dictionary is not sharedbetween the two nodes. Thus, the second node stores the same datasegment as an original segment. Sharing the entire dictionary would bepossible with a locking mechanism and a mechanism for coalescing updatesfrom multiple nodes. However, such mechanisms can be complicated andadversely impact performance.

Consequently, a work partitioning scheme can be applied based on segmentID or hash value ranges for various data segments. Ranges of hash valuesare assigned to different nodes within the cluster. If a node isprocessing a data segment which has a hash value which maps to anothernode, it will contact the other node that owns the range to find out ifthe data segments already exist in a datastore.

FIG. 5 illustrates multiple dictionaries assigned to different segmentID or hash ranges. Although hash ranges are described, it should berecognized that the dictionary index can be hash ranges, referencevalues, or other types of keys. According to various embodiments, thehash values are SHA1 hash values. In particular embodiments, dictionary501 is used by a first node and includes hash ranges from 0x0000 00000000 0000-0x0000 0000 FFFF FFFF. Dictionary 551 is used by a second nodeand includes hash ranges from 0x0000 0001 0000 0000-0X0000 0001 FFFFFFFF. Hash values 511 within the range for dictionary 501 arerepresented by symbols a, b, and c for simplicity. Hash values 561within the range for dictionary 551 are represented by symbols i, j, andk for simplicity. According to various embodiments, each hash value indictionary 501 is mapped to a particular storage location 521 such aslocation 523, 525, or 527. Each hash value in dictionary 551 is mappedto a particular storage location 571 such as location 573, 575, and 577.

Having numerous small segments increases the likelihood that duplicateswill be found. However, having numerous small segments decreases theefficiency of using the dictionary itself as well as the efficiency ofusing associated filemaps and datastore suitcases.

FIG. 6A illustrates one example of a non-container file. According tovarious embodiments, container files such as ZIP files, archives,productivity suite documents such as .docx, .xlsx, etc., includemultiple objects of different types. Non-container files such as imagesand simple text files typically do not contain disparate objects.

According to various embodiments, it is recognized that certain types ofnon-container files do not benefit from having a segment size smallerthan the size of the file itself. For example, many image files such as.jpg and .tiff files do not have many segments in common with other .jpgand .tiff files. Consequently, selecting small segments for such filetypes is inefficient. Consequently, the segment boundaries for an imagefile may be the boundaries for the file itself. For example,noncontainer data 601 includes file 603 of a type that does not benefitfrom finer grain segmentation. File types that do not benefit from finergrain segmentation include image files such as .jpg, .png, .gif, .and.bmp files. Consequently, file 603 is provided with a single segment605. A single segment is maintained in the deduplication dictionary.Providing a single large segment encompassing an entire file can alsomake compression of the segment more efficient. According to variousembodiments, multiple segments encompassing multiple files of the sametype are compressed at the same time. In particular embodiments, onlysegments having data from the same type of file are compressed using asingle compression context. It is recognized that specializedcompressors may be applied to particular segments associated with thesame file type.

FIG. 6B illustrates one example of a container file having multipledisparate objects. Data 651 includes a container file that does benefitfrom more intelligent segmentation. According to various embodiments,segmentation can be performed intelligently while allowing compressionof multiple segments using a single compression context. Segmentationcan be implemented in an intelligent manner for deduplication whileimproving compression efficiency. Instead of selecting a single segmentsize or using a sliding segment window, file 653 is delayered to extractfile components. For example, a .docx file may include text, images, aswell as other container files. For example, file 653 may includecomponents 655, 659, and 673. Component 655 may be a component that doesnot benefit from finer grain segmentation and consequently includes onlysegment 657. Similarly, component 659 also includes a single segment661. By contrast, component 673 is actually an embedded container file663 that includes not only data that does benefit from additionalsegmentation but also includes component 673. For example, data 665 mayinclude text. According to various embodiments, the segment size fortext may be a predetermined size or a dynamic or tunable size. Inparticular embodiments, text is separated into equal sized segments 667,669, and 671. Consequently, data may also include a non-text object 673that is provided with segment boundaries aligned with the objectboundaries 675.

FIG. 7 illustrates one example of a computer system. According toparticular example embodiments, a system 700 suitable for implementingparticular embodiments of the present invention includes a processor701, a memory 703, an interface 711, and a bus 715 (e.g., a PCI bus).When acting under the control of appropriate software or firmware, theprocessor 701 is responsible for such tasks such as optimization.Various specially configured devices can also be used in place of aprocessor 701 or in addition to processor 701. The completeimplementation can also be done in custom hardware. The interface 711 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude Ethernet interfaces, frame relay interfaces, cable interfaces,DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 700 uses memory703 to store data and program instructions and maintained a local sidecache. The program instructions may control the operation of anoperating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

Although many of the components and processes are described above in thesingular for convenience, it will be appreciated by one of skill in theart that multiple components and repeated processes can also be used topractice the techniques of the present invention.

While the invention has been particularly shown and described withreference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the invention. It is therefore intended that the invention beinterpreted to include all variations and equivalents that fall withinthe true spirit and scope of the present invention.

What is claimed is:
 1. A method, comprising: determining whether a firstportion of a first file is less frequently accessed than a secondportion of the first file; based on a determination that the firstportion of the first file is less frequently accessed than the secondportion of the first file, performing optimization on the first portionof the first file more aggressively to achieve a higher reduction ratioon the first portion of the first file than on the second portion of thefirst file, wherein optimization comprises deduplication of data in thefirst file.
 2. The method of claim 1, wherein access comprises processorread and write access.
 3. The method of claim 1, further comprisingmonitoring access to a plurality of portions comprises maintainingaccess time information.
 4. The method of claim 1, further comprisingmaintaining a heat index.
 5. The method of claim 1, wherein optimizationfurther comprises compression.
 6. The method of claim 1, wherein thesecond portion of the first file is left unoptimized.
 7. The method ofclaim 1, wherein a third portion of the first file is accessed morefrequently than the second portion of the first file.
 8. The method ofclaim 1, wherein an optimization scale is applied to determine theaggressiveness of optimization based on the frequency of access.
 9. Themethod of claim 1, wherein a heat index is used to determine theaggressiveness of optimization based on the frequency of access.
 10. Themethod of claim 1, wherein more aggressive optimization results insmaller file sizes.
 11. An apparatus, comprising: a processor configureddetermine whether a first portion of a first file is less frequentlyaccessed than a second portion of the first file; wherein the processoris further configured to optimize the first file; wherein optimizationis performed on the first portion of the first file more aggressivelybased on a determination that the first portion of the first file isless frequently accessed than the second portion of the first file toachieve a higher reduction ratio on the first portion of the first filethan on the second portion of the first file, wherein optimizationcomprises deduplication of data in the first file.
 12. The apparatus ofclaim 11, wherein access comprises processor read and write access. 13.The apparatus of claim 11, wherein the processor is further configuredto monitor access to a plurality of portions comprises maintainingaccess time information.
 14. The apparatus of claim 11, wherein theprocessor is further configured to maintain a heat index.
 15. Theapparatus of claim 11, wherein optimization further comprisescompression.
 16. The apparatus of claim 11, wherein the second portionof the first file is left unoptimized.
 17. The apparatus of claim 11,wherein a third portion of the first file is accessed more frequentlythan the second portion of the first file.
 18. The apparatus of claim11, wherein an optimization scale is applied to determine theaggressiveness of optimization based on the frequency of access.
 19. Theapparatus of claim 11, wherein a heat index is used to determine theaggressiveness of optimization based on the frequency of access.
 20. Anon-transitory computer readable medium, comprising: computer code fordetermining whether a first portion of a first file is less frequentlyaccessed than a second portion of the first file; and computer code foroptimizing the first file; wherein optimization is performed on thefirst portion of the first file more aggressively based on adetermination that the first portion of the first file is lessfrequently accessed than the second portion of the first file to achievea higher reduction ratio on the first portion of the first file than onthe second portion of the first file, wherein optimization comprisesdeduplication of data in the first file.